基于k-NN和SCATS交通数据的路段行程时间估计方法

Travel Time Estimation Method Using SCATS Traffic Data Based on k-NN Algorithm

为了改善利用SCATS交通数据估计路段行程时间的效果,通过分析SCATS实际交通数据获取时间间隔不一致的特征,构建了SCATS交通数据虚拟时间序列,将利用因子分析法提取的累计贡献率在85%以上的主因子作为交通模式特征向量的构成要素,用欧氏距离作为当前交通模式特征向量和历史交通模式特征向量相似性的测度指标,以路段行程时间估计误差最小为目标选取当前交通模式的近邻数,对交通模式之间距离的倒数进行归一化处理,确定了相似交通模式的行程时间权重,设计了基于SCATS交通数据的路段行程时间估计方法.实例结果表明:与多元线性回归方法相比,本文方法估计的路段行程时间平均绝对误差、平均绝对百分比误差和均方根误差分别平均减少了9.68 s、8.07%和4.5 s.

A new method was designed to improve the effect of travel time estimation using SCATS traffic data. In this method, by analyzing the characteristics of SCATS traffic data that their acquisition intervals are not strictly consistent, the virtual time series of SCATS traffic data was constructed first. Then, factors of cumulative squared loading over 85% extracted by factor analysis were included in the traffic state feature vector, and the Euclidean distance was used to measure the closeness between current traffic state and historical traffic state. Finally, the number of nearest neighbors that correspond to the minimum error of travel time estimation was selected, and the weights of k-nearest neighbors were identified by normalizing the reciprocal of the distance between traffic states, based on which the travel time estimation method was established. The results indicate that compared with the multiple linear regression （MLR） method, the proposed method can reduce the mean absolute error （MAE）, mean absolute percentage error （MAPE） and root-mean-square error （RMSE） of travel time estimation by an average of 9.68 s, 8.07% and 4.5 s, respectively.